(1) Background: Biomarkers might play a significant role in predicting the clinical outcomes of patients with ovarian cancer. By analyzing lipid metabolism genes, future perspectives may be uncovered; (2) Methods: RNA-seq data for serous ovarian cancer were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The non-negative matrix factorization package in programming language R was used to classify molecular subtypes of lipid metabolism genes and the limma package in R was performed for functional enrichment analysis. Through lasso regression, we constructed a multi-gene prognosis model; (3) Results: Two molecular subtypes were obtained and an 11-gene signature was constructed (PI3, RGS, ADORA3, CH25H, CCDC80, PTGER3, MATK, KLRB1, CCL19, CXCL9 and CXCL10). Our prognostic model shows a good independent prognostic ability in ovarian cancer. In a nomogram, the predictive efficiency was notably superior to that of traditional clinical features. Related to known models in ovarian cancer with a comparable amount of genes, ours has the highest concordance index; (4) Conclusions: We propose an 11-gene signature prognosis prediction model based on lipid metabolism genes in serous ovarian cancer.

Macrophages are dynamic cells susceptible to the local microenvironment which includes tumor-associated macrophages (TAMs) in cancers. TAMs are a collection of heterogeneous macrophages, including M1 and M2 subtypes, shaped by various activation modes and labeled with various markers in different tumors. CCL22+-infiltrating cells are thought to be significantly associated with the prognosis of cervical cancer patients. Moreover, CCL22 is an established marker of M2a macrophages. Although the phenotypic identification of M1 and M2 macrophages is well established in mice and human macrophages cultured in a medium with fetal calf serum (FCS), fewer studies have focused on M2 subtypes. In addition, the question of whether CCL22 affects polarization of M2a macrophages remains unanswered. This study constructed a co-culture system to shape TAMs in vitro. We found that CCL22 was mainly secreted by TAMs but not cervical cancer cell lines. Human peripheral blood monocytes were differentiated into uncommitted macrophages (M0) and then polarized to M1, M2a, M2b, and M2c macrophages using LPS plus IFNr, IL-4, LPS plus IL1β, and IL-10, respectively. Using flowcytometry, we found CD80++ was the marker of M1 and M2b, CD206++ was the marker of M2a, and CD163++ was the marker of M2c, compared with M0 macrophages. By regulating CCL22, we found that the mean fluorescence intensity (MFI) of CD206 in TAMs was significantly affected compared to the control group. Therefore, CCL22 could polarize TAMs of cervical cancer toward M2a macrophages. In conclusion, our study revealed that CCL22 could be a therapeutic target for cervical cancer, which might be because of its role in regulating macrophage polarization.

The chemokine CCL22 recruits regulatory T (T-reg) cells into tumor tissues and is expressed in many human tumors. However, the prognostic role of CCL22 in cervical cancer (CC) has not been determined. This study retrospectively analyzed the clinical significance of the expression of CCL22 and FOXP3 in 230 cervical cancer patients. Immunohistochemical staining analyses of CCL22 and FOXP3 were performed with a tissue microarray. Double immunofluorescence staining, cell coculture, and ELISA were used to determine CCL22 expressing cells and mechanisms. The higher number of infiltrating CCL22+ cells (CCL22^high) group was associated with lymph node metastasis (p = 0.004), Fédération Internationale de Gynécologie et d’Obstétrique (FIGO) stages (p = 0.010), therapeutic strategies (p = 0.007), and survival status (p = 0.002). The number of infiltrating CCL22+ cells was positively correlated with that of infiltrating FOXP3+ cells (r = 0.210, p = 0.001). The CCL22^high group had a lower overall survival rate (OS), compared to the CCL22^low group (p = 0.001). However, no significant differences in progression free survival (PFS) were noted between the two groups. CCL22^high was an independent predictor of shorter OS (HR, 4.985; p = 0.0001). The OS of the combination group CCL22^highFOXP3^high was significantly lower than that of the combination group CCL22^lowFOXP3^low regardless of the FIGO stage and disease subtype. CCL22^highFOXP3^high was an independent indictor of shorter OS (HR, 5.284; p = 0.009). The PFS of group CCL22^highFOXP3^high was significantly lower than that of group CCL22^lowFOXP3^low in cervical adenocarcinoma, but CCL22^highFOXP3^high was not an independent indicator (HR, 3.018; p = 0.068). CCL22 was primarily expressed in M2-like macrophages in CC and induced by cervical cancer cells. The findings of our study indicate that cervical cancer patients with elevated CCL22+ infiltrating cells require more aggressive treatment. Moreover, the results provide a basis for subsequent, comprehensive studies to advance the design of immunotherapy for cervical cancer.

(1) Background: The tumor microenvironment is involved in the growth and proliferation of malignant tumors and in the process of resistance towards systemic and targeted therapies. A correlation between the gene expression profile of the tumor microenvironment and the prognosis of ovarian cancer patients is already known. (2) Methods: Based on data from The Cancer Genome Atlas (379 RNA sequencing samples), we constructed a prognostic 11-gene signature (SNRPA1, CCL19, CXCL11, CDC5L, APCDD1, LPAR2, PI3, PLEKHF1, CCDC80, CPXM1 and CTAG2) for Fédération Internationale de Gynécologie et d’Obstétrique stage III and IV serous ovarian cancer through lasso regression. (3) Results: The established risk score was able to predict the 1-, 3- and 5-year prognoses more accurately than previously known models. (4) Conclusions: We were able to confirm the predictive power of this model when we applied it to cervical and urothelial cancer, supporting its pan-cancer usability. We found that immune checkpoint genes correlate negatively with a higher risk score. Based on this information, we used our risk score to predict the biological response of cancer samples to an anti-programmed death ligand 1 immunotherapy, which could be useful for future clinical studies on immunotherapy in ovarian cancer.